System, process, and devices for real-time brain monitoring in panic and anxiety disorder

ABSTRACT

Systems, processes and devices for real-time brain monitoring for anxiety to generate and control an interface of a display device with a visual representation of a Brain Value Index for anxiety, a connectivity map and treatment guidance. Systems, processes and devices for real-time brain monitoring capture sensor data, process the data and dynamically update the interface in real-time.

FIELD

The improvements generally relate to the field of monitoring patientswith anxiety disorder using sensors and computing devices.

INTRODUCTION

Anxiety disorders are commonly occurring mental health disordersaccounting for 18% of mental illnesses. Anxiety can be described as anemergent property of brain electrical activity that recruits neuronalnetworks to create the physiological response associated with anxiety.Neurophysiologic recordings of brain activity may demonstratefluctuating patterns of cellular interactions. Alcohol, which alsoalters neuronal networks, is commonly used to self-medicate and dealwith the symptoms of anxiety.

SUMMARY

In accordance with an aspect, there is provided a system for real-timebrain monitoring. The system has a plurality of sensors for acquisitionof (near) real-time raw sensor data for monitoring a patient's brain,each sensor corresponding to a channel. The system has a collectordevice coupled to the plurality of sensors for pre-processing thereal-time raw sensor data. The system has a server with an acquisitionunit to receive sensor data from the collector device. The server has aprocessor to compute, using the sensor data, a connectivity matrixhaving connectivity values, a connectivity value for each pair ofchannels, and a real-time brain value index corresponding to a real-timebrain state of the patient. The server has a presentation unit togenerate visual elements for an interface in real-time, the visualelements representing the real-time brain value index to depict thebrain state of the patient and a connectivity map for the connectivitymatrix, the connectivity map visually indicating the channels monitoredby the sensors and a connecting line between a pair of channelsrepresenting a strength of connection between the pair of channels, theserver system having a display controller to issue control commands toupdate the interface using the generated visual elements. The system hasa display device to display and update the interface with the visualelements based on the issued control commands from the server.

In some embodiments, the server computes, for each pair of channels, aphase synchronization value for an angle between the respective pair ofchannels using the sensor data for the respective pair of channels,wherein entries of the connectivity matrix are the phase synchronizationvalues the pairs of channels.

In some embodiments, the server generates a boolean connectivity matrixbased on the connectivity matrix, such that an entry of the booleanconnectivity matrix is 0 if a corresponding connectivity value is lowerthan a threshold value, and 1 if a corresponding connectivity value ishigher than the threshold value, wherein the server computes thethreshold value from sensor data for a normal adult with eyes open,wherein a connected channel is defined as an entry that is 1, whereinthe server generates the brain value index using the booleanconnectivity matrix.

In some embodiments, the brain value index may be computed based a totalnumber of possible pairs of channels given a specific channel montageN=Nc!/p!(Nc−p)!, Nc being a number of channels, p being a number ofconnected pairs of channels, p being calculated using a threshold valueand the connectivity values of the connectivity matrix.

In accordance with one aspect, there is provided a system for real-timebrain monitoring of anxiety having a plurality of sensors foracquisition of (near) real-time raw sensor data for a patient's brain; acollector device coupled to the plurality of sensors for pre-processingthe real-time raw sensor data; a server for processing the real-time rawsensor data to compute a connectivity matrix for brain entropy, areal-time brain value index and treatment guidance, the server systemhaving a display controller to issue control commands to continuouslyupdate an interface in real-time, the brain value index corresponding toa real-time brain state; and a display device having the interface togenerate and update a visual representation of the real-time brain valueindex and the treatment guidance based on the issued control commandsfrom the server.

In some embodiments, the server computes the connectivity matrix forbrain entropy.

In some embodiments, the treatment guidance provides a monitoring stateof anxiety or an intervention state.

In some embodiments, the display device provides feedback data to refineor update the processing by the server.

In some embodiments, the server computes phase synchronization for eachchannel pair angle, where the entries of the connectivity matrix arevalues for each pair combination.

In some embodiments, the server computes the connectivity matrix as aBoolean connectivity matrix where the entries are, 0 if a correspondingindex is lower than a threshold, and 1 if higher, where the servercomputes a threshold from the average of indices of normal adults witheyes open, where connected channels are defined as entries 1.

In some embodiments, the brain value index or functionality index orbrain viability index (BVI) calculation may be defined using a totalnumber of possible connections given a specific channel montage asN=Nc!/p!(Nc−p)! (Nc is 8 to 12) where Nc is the number of channels orelectrodes, and where p (the number of connected pairs of channels) iscalculated for that instance using a threshold value, wherein the serversystem computes an entropy value associated of the p values andcalculates a normalized entropy to a value between 0 and 1.

In some embodiments, the server implements machine learning to computethe brain value index based on historical data for the patient or otherpatients.

In some embodiments, the server implements machine learning to generaterecommended treatments as part of the treatment guidance based onhistorical data for the patient or other patients.

In some embodiments, the real-time raw sensor data is linked with apatient identifier and time indicia.

In accordance with another aspect, there is provided a processing devicefor real-time brain monitoring having a network interface foracquisition of real-time raw sensor data for a patient's brain; a serverfor processing the real-time raw sensor data to compute a connectivitymatrix for brain entropy, a real-time brain value index and treatmentguidance, the server system having a display controller to issue controlcommands to an interface, the brain value index corresponding to areal-time brain state; a storage device for storing computed real-timebrain value indices; and a display device having the interface togenerate and update a visual representation of the real-time brain valueindex and the treatment guidance based on the issued control commandsfrom the server.

In accordance with another aspect, there is provided a process forreal-time brain monitoring of anxiety involving acquiring real-time rawsensor data for a patient's brain from a plurality of sensors;pre-processing the real-time raw sensor data; processing, at a server,the real-time raw sensor data to compute a connectivity matrix for brainentropy, a real-time brain value index and treatment guidance, theserver system having a display controller to issue control commands toan interface, the brain value index corresponding to a real-time brainstate; generating and updating, on a display device having theinterface, a visual representation of the real-time brain value indexand the treatment guidance based on the issued control commands from theserver.

In various further aspects, the disclosure provides correspondingsystems and devices, and logic structures such as machine-executablecoded instruction sets for implementing such systems, devices, andmethods.

In this respect, before explaining at least one embodiment in detail, itis to be understood that the embodiments are not limited in applicationto the details of construction and to the arrangements of the componentsset forth in the following description or illustrated in the drawings.Also, it is to be understood that the phraseology and terminologyemployed herein are for the purpose of description and should not beregarded as limiting.

Many further features and combinations thereof concerning embodimentsdescribed herein will appear to those skilled in the art following areading of the instant disclosure.

DESCRIPTION OF THE FIGURES

In the figures,

FIG. 1 is a diagram of a system for real-time brain monitoring accordingto some embodiments;

FIG. 2 is a diagram of a system for real-time brain monitoring accordingto some embodiments;

FIG. 3 is a flow chart diagram of a process for real-time brainmonitoring according to some embodiments;

FIG. 4 shows an example interface with a visual representation forreal-time brain monitoring according to some embodiments;

FIG. 5 shows an example interface with a visual representation providinga graph of brain connectivity (left), functionality index (right top) ornormalized entropy, and raw EEG signals per channel (right bottom)according to some embodiments; and

FIG. 6 shows an example hardware setup for sensors.

FIG. 7 shows an example interface with a visual representation of aconnectivity map, connectivity map values, functionality index, and rawEEG data according to some embodiments.

FIG. 8 is an example interface with a visual representation for brainvalue index values for anxiety as described herein.

FIG. 9 shows an example interface with a visual representation of aconnectivity map, connectivity map values, functionality index, and rawEEG data according to some embodiments.

FIG. 10 shows an example graph for a patient with panic disorder.

FIG. 11 shows an example graph for a patient with panic disorder.

FIG. 12 shows an example graph for a patient with panic disorder.

FIG. 13 shows an example graph for a patient with panic disorder.

FIG. 14 shows an example graph for a patient with panic disorder.

FIG. 15 shows an example graph for a patient with panic disorder.

FIG. 16 shows an example graph for a patient with panic disorder.

FIG. 17 shows an example graph for a patient with panic disorder.

FIG. 18 shows an example graph for a patient with panic disorder.

DETAILED DESCRIPTION

Embodiments described herein relate to systems, processes and devicesfor real-time brain monitoring using sensors and signal processingrules. As an example, the real-time monitoring may detect differentbrain states of a patient for anxiety disorders. The systems, processesand devices for real-time brain monitoring may use sensors to acquireneurological or brainwave signal data and process the signal data tocompute a real-time, changing brain state index or brain value index.The system may automatically suggest treatment for anxiety disordersbased on the brain value index. Each anxiety disorder can be associatedwith a threshold value or range of values configured for a patient. Thecomputed brain value index can be compared with the threshold value orrange of values to determine an anxiety disorder, for example.

The following provides examples of patient data that highlight thefunctionality of the system. A detailed description of the graphicaloutput is first provided. The system includes a graphical display andoutput from the EEG is displayed graphically for the user and an exampleinterface 700 with no data is provided in FIG. 7.

The interface 700 provides the display seen by the user before recordingstarts. In panel 716 8 electrodes are shown as an example deviceconfiguration starting with T3 (left anterior temporal electrode) at thetop and ending with T6 (right posterior temporal electrode at thebottom. The waveforms will be seen in panel 1 as they acquired. Thewindow allows for 10 seconds of waveforms and is refreshed every second.The waveforms appear from right to left. The adjacent panel 712 entitled“Raw EEG Data” can show the voltage in microvolts as an integer valuefor each of the electrodes. This panel 712 can be hidden in someembodiments. Panel 708 shows the output from the next step afteracquisition. Phase synchrony, which quantifies the connectivity betweenall possible pairs of electrodes (e.g. 0 and 1; 1 and 2, . . . 6 and 7)is an integer value between 0 and 1 in some example embodiments. Inaddition to a numeric output which can be hidden, connectivity isdepicted as solid lines between the 8 electrodes on the head map 706that have a phase synchrony index at least >0.45 in some embodiments.Further the lines can be weighted as shown in the table below.

Connectivity range Colour and thickness 0.45 to <0.6  Light grey, 1point 0.6 to <0.8 Medium grey, 1.5 point 0.8 to <0.9 Dark grey, 2 point0.9 to 1.0  Black, 3 point

Panel 714 provides subsequent analytics with the BVI shown as a numberbetween 0 and 100 and a round cursor or indicator 704 that moves to theright or the left side of the curve 702. Decrease in the BVI on theright side of the curve 702 is associated with greater connectivitybetween electrodes, while a decrease in the BVI down the left side ofthe curve 702 is associated with less connectivity between electrodes.

At the bottom of the interface 700, panel 710 is shown with a feature ofthe device whereby the type of recording is identified. If the recordingis currently being acquired, the identifier is the date and time of therecording. If previously obtained recordings are being reviewed, theyare identified as “Simulation” followed by the date and time of theactual recording. The second feature at the bottom of the panel,indicator 718, is the “Start” and “Stop” recording functions.

An example use case compares a normal brain function to signals capturedduring a panic attack. This example compares the brain function of a 53year old female who was experiencing a panic attack with her baseline,non-anxiety recording and that of a gender and age matched control withno history of anxiety disorder. The subject experiencing aself-described panic attack scored 70 on the PROMIS EmotionalDistress—Anxiety—Short Form. This corresponds to severe anxiety. The ageand gender matched control subject scored 36.3 using the sameinstrument. This can correspond to none to slight anxiety.

FIG. 8 is an example interface with a visual representation for brainvalue index values for anxiety as described herein. An indicator for thebrain value index can move along the curve 902 to different positionsdepending on the value. Different regions on the curve 902 can representdifferent brain states or functions, such as a normal awake brainfunction, migraines, REM sleep, and so on. An anxiety state can beassociated with a threshold value or range of values configured for apatient. The values can be determined during a calibration stage, forexample. The values can be determined relative to the baseline or“normal” brain state for the patient, as another example. The computedbrain value index can be compared with the threshold value or range ofvalues to determine an anxiety state, for example. The threshold valueor range of values can be linked to different positions on the curve 902to indicate the corresponding anxiety state. The indicator can provide avisual representation of the corresponding real-time anxiety state.

FIG. 9 shows an interface with a graphical display of normal brainfunction and anxiety disorder. The interface depicts one time epoch inthe 65 seconds of recording of a 53 year old woman, which isrepresentative of her panic attack. The EEG wave forms (bottom righthand corner) are normal for age. The BVI or Functionality Index (topright hand corner) is 59.17 on the left of side of the curve for thisexample. A Connectivity Map (left hand side panel) shows someinterhemispheric (between the hemispheres) connectivity to differentdegrees, between pairs of electrodes. For example, for F7 and F8, phasesynchrony index >0.9; T3 and T4, phase synchrony index 0.47 to 0.6. Ananxiety disorder can be associated with a threshold value or range ofvalues configured for a patient. The values can be determined during acalibration stage when the patient is experiencing a panic or anxietystate, for example. The values can be determined relative to thebaseline or “normal” (or not anxious) brain state for the patient, asanother example. The computed brain value index can be compared with thethreshold value or range of values to determine an anxiety disorder, forexample.

FIG. 10 shows an example graph for a patient with panic disorder thatcan be referred to as a Functionality Index time series. The graphdepicts the comparison in Functionality Indices (y axis) over the lengthof each 65 second recording (x axis). The patient experiencing the panicattack has predominantly lower Functionality Index values during theepisode, than when she is not in a state of anxiety. The difference inmean Functionality Indices between the anxiety state (70.3) and baseline(90.0) is statistically significant with t-test p<0.001 as an example.These recorded values can be used to determine a threshold value orrange of values configured for a patient. The computed brain value indexcan be compared with the threshold value or range of values to determinean anxiety disorder or anxiety state, for example.

FIG. 11 shows an example graph for a patient with panic disorder thatcan be referred to as Functionality Index frequencies. The graph showsthe frequency with which a value occurs in each of the possible 14indices on each side of the 100. The Indices corresponding to 90, 94 and96 are grouped together and those indices corresponding to 98, 99, and100 are grouped together on either side of the curve. In the subject'sbaseline (not anxious) state, the indices approach a normal distributionwith the majority of the indices in the 90 to 96 range, just left ofcentre. In contrast, the frequency pattern during the panic attack isskewed to the left of the curve with the majority of indices at 59, 67and 84. A lower brain value index range may indicate an anxiety statethan the baseline brain value index range. Historical anxiety data canbe used to configure the threshold values or range of values for theanxiety states of the patient.

Another example compares the brain function of the same 53 year oldfemale from the previous example who was experiencing a panic attackwith that of a gender and age matched control with no history of anxietydisorder. As in the previous example, the subject experiencing aself-described panic attack scored 70 on the PROMIS EmotionalDistress—Anxiety—Short Form. This corresponds to severe anxiety. Athreshold value or range of values can indicate this state of severeanxiety. The age and gender matched control subject scored 36.3 usingthe same instrument. This corresponds to none to slight anxiety.

FIG. 12 shows an example graph for a patient with panic disorder thatcan be referred to as Functionality time series. The graph compares theFunctionality Indices over 65 seconds (x axis) for an age and gendermatched control subject (solid black line and marker) with that of thepatient in example 1 during her panic attack(unfilled marker and dottedblack line). The difference in mean Functionality Indices between theanxiety subject (70.3) and control subject (91.2) is statisticallysignificant with t-test p<0.001.

FIG. 13 shows an example graph for a patient with panic disorder thatcan be referred to as Functionality Index frequency. The graph showsanother method of comparison of the patient with anxiety and the age andgender matched control with respect to Functionality Index valuefrequencies. The patient with anxiety has lower Functionality Indexvalues and they are on the left hand side of the curve which correspondsto a “disconnected” brain. The lower Functionality Index values cancorrespond to the threshold values or range of values for the anxietystate of the patient.

FIG. 14 shows an example graph for a patient with panic disorder thatcan be referred to as self-medication time series. The graph shows thedifference in Functionality indices in the same subject during her panicattack (solid black marker and solid black line) and duringself-medication with alcohol (ETOH—open marker with dotted black line).Blood alcohol level at the time of the recording, when the subjectdeclared that the panic symptoms had resolved was estimated at 0.03,based on a weight of 116 lbs and 30 minutes following the equivalent of1 drink. Functionality indices are both lower than baseline (no anxiety,no alcohol) at 70.3 for the panic and 74.2 for the ETOH). The capturedanxiety data can be used to configure the threshold values or range ofvalues for the anxiety states of the patient.

FIG. 15 shows an example graph for a patient with panic disorder thatcan be referred to as frequency comparison. The graph shows thefrequency distribution of functionality indices for the panic state(solid black marker) and the ETOH treatment state (white filled marker).While similar in distribution, the majority of Functionality Indices arein the 67 to 96 range for the ETOH state, while those of the panic stateare clustered in the 59 to 81 range. These patterns can be input formachine learning to differentiate panic from intoxication. The machinelearning output can be used to configure the threshold values for theanxiety state.

The same subject had a second panic attack within 7 days of the first.It was described similarly with feelings of fear, gastrointestinal upsetand muscle tension. The subject experiencing a self-described panicattack scored 67.7 on the PROMIS Emotional Distress—Anxiety—Short Form.This corresponds to moderate anxiety. The average Functionality Indexwas 74.3, which was not statistically significantly different from thefirst panic attack, but statistically significantly different (P<0.01)from the baseline.

FIG. 16 shows an example graph for a patient with panic disorder. Thegraph compares the frequency of the Functionality Indices in the samesubject during the initial panic attack, during baseline and the secondpanic event.

FIG. 17 shows an example graph for a patient with panic disorder. Thecaptured anxiety data can be used to configure the threshold values orrange of values for the anxiety states of the patient.

FIG. 18 shows an example graph for a patient with panic disorder. Thecaptured anxiety data can be used to configure the threshold values orrange of values for the anxiety states of the patient.

FIG. 1 shows an example system for real-time brain monitoring. Thesystem may include sensors 102 coupled to a patient for real-time brainstate monitoring. The sensors 102 may include electroencephalography(EEG) sensors (e.g. electrodes) to record electrical activity of thebrain when placed on the scalp of the patient, for example. The sensors102 may generate brainwave signal data for the patient. The sensors 102measure voltage fluctuations resulting from spontaneous electricalactivity, neural oscillations or brainwaves over a period of time.Accordingly, the brainwave signal data may be time coded. Sensors 102may also include other types of biological sensors to generateadditional biological or physiological data signals such as heart rate,temperature, and so on. The sensors 102 data feeds include time codesthat can be cross-referenced to timecodes of other data feeds.

The system includes a collector device 104 coupled to the sensors 102for pre-processing the real-time raw sensor data. A server system 100processes the real-time raw sensor data to compute a connectivitymatrix, a real-time brain value index, treatment guidance and otherdata. The server system 100 has a display controller to issue controlcommands to a display device 106 to continuously update an interface inreal-time. The brain value index (BVI) can correspond to a real-timebrain state. The brain state can correspond to an anxiety state (ordifferent levels of anxiety). The anxiety state can correspond to athreshold value or a range of threshold values. There can be differentanxiety states (no, low, medium, severe, panic attack). The displaydevice 106 has an interface to generate and update a visualrepresentation of the real-time BVI and the treatment guidance based onthe issued control commands from the server.

Sensor 102 can refer to an electrode for gathering physiologicalinformation from a patient or control subject. Sensors 102 can refer tochannels and are located on a portion of a patient's brain. In someinstances, sensor 102, electrode and channel may be usedinterchangeably. Example sensors 102 can include an EKG (heart rate),EEG (brainwave) and other bio-signal devices. Montage can refer to aspecific arrangement of EEG electrodes on the scalp. For example, therecan be the international 10-20 montage of 10 to 20 electrodes or asubset of these.

In some embodiments, the sensors 102 can be EEG sensors to acquire rawEEG data from the patient. The sensors 102 include electrodes to recordelectrical activity of a brain of the patient as brainwave signals orraw EEG data. The sensors 102 can be placed at different locations on apatient's scalp or head to capture the brainwave signals. The EEG datacan refer to the recording of electrical activity of a brain (e.g.brainwave signals) captured by the sensors 102 over a period of time. Asnoted, a sensor 102 includes an electrode configured to capturebrainwave activity. The electrode can be referred to as an EEG channel.The sensors 102 can include multiple EEG channels to capture brainwavesignals. The electrodes can be positioned on different locations of thepatient's scalp and head to represent different channels. The EEGchannels can refer to different locations on the patient. The sensors102 can involve different EEG channels or a different arrangement orlayout of positions of electrodes on the patient's scalp and head. Thesensors 102 provide an electrode network or array that evolves dependingon the desired number of EEG channels and the position of the EEGchannels relative to the patient's scalp or head. Different parts of thebrain serve different functions and placement of the electrodes ondifferent parts of the brain can capture brainwave data signals thatcorrespond to different cognitive functions. In some embodiments, thesensors 102 are configured for acquisition of (near) real-time rawsensor data for a patient's brain (e.g. brainwave signal data).

In some embodiments, the system includes a wearable device withparticular sensor 102 or electrode placements to standardize thepositioning of sensors 102 or electrodes to access brainwave signal dataat specific brain locations that serve specific brain functions. Thewearable device can have attachments for electrodes at particularpositions and the electrodes can be removably attached to the wearabledevice at the different positions to provide a variety of attachmentoptions and configurations for positioning the electrodes. By way ofexample, electrode placements can capture brainwave data signalsrepresenting activity at the prefrontal cortex and frontal lobe. Alocation or site of an electrode or EEG channel can be identified orreferenced by a letter for the lobe and a number for the hemispherelocation. For example, the letters F, T, C, P and O stand for frontal,temporal, central, parietal, and occipital lobes, respectively. Evennumbers can refer to electrode positions on the right hemisphere,whereas odd numbers refer to those on the left hemisphere. A “z” (zero)can refer to an electrode placed on the midline. Example EEG channelscan include T3, F7, F8, T4, T5, O1, O2, T6. In addition to thesecombinations, the letter codes A, Pg and Fp can identify the earlobes,nasopharyngeal and frontal polar sites respectively. Two anatomicallandmarks can be used for the positioning of the EEG electrodes. Onelandmark is the nasion which is a depressed area between the eyes, justabove the bridge of the nose and another landmark is the inion, which isthe lowest point of the skull from the back of the head and is normallyindicated by a prominent bump.

For ease of application, we employ the coronal EEG montage whichcombines both ease of application to the scalp and provides importantinformation from frontal, temporal and occipital lobes. The frontal andtemporal regions are particularly vulnerable in all types of braininjury. This montage coupled to server system 100 can provideinformation on functioning within a hemisphere when examiningrelationships between frontal and occipital electrodes. This alsoprovides information on functioning across hemispheres when therelationship between pairs of electrodes is examined: F7 and F8, T3 andT4, and so on. The eight electrode montage can be processed in real-timeand results in 28 possible electrode pairs (N=Nc!/p!(Nc−p)!. Where p isthe number of connected pairs and Nc is the number of electrodes orsensors 102 in the system (8 in this example). For this example therecan be 14 different functionality or Brain Value Indices, the numberbeing constrained by the number of electrodes and the arrangement, aswill be explained herein. Fewer electrodes can result in fewer indices.More electrodes, such as the 144 channels of magenetoencephalography(MEG) produce 10296 possible pairs. An example can be N=Nc!/2!(Nc−2)!.They can be processed retrospectively using the processes describedherein to calculate the phase synchrony, connectivity and entropyindices.

FIG. 6 shows an example hardware setup for sensors 102. The sensors 102or electrodes can be arranged as a subset of the international 10-20montage for EEG electrode placement. The montage is coronal in that whenlooking down at the head of the subject, the right side of the head fromthe nose (Nasion) to the back of the head (Inion) is identified by evennumbers and the left side of the head, by odd numbers. The electrodesthus follow the circumference of the head in the horizontal plane. Theletters correspond to the lobes that underlie the electrodes: Frontal,(F), Temporal (T) and Occipital (O). The ground (G) electrode is in themiddle of the forehead and the reference electrode is placed on eitherear. For this example, the right ear may be used for the ground. Theeight electrodes are numbered starting with 0 that corresponds to T3which is left anterior temporal and the numbers continue across thefront of the head to the right anterior temporal electrode. Thenumbering resumes starting with the left posterior temporal electrode(T5) and continues across the back of the head to the right posteriortemporal electrode.

In some embodiments, the real-time raw sensor data is linked with apatient identifier and time indicia. For example, the recording can beautomatically saved with a file name of “DATE-TIME-LENGTH.bin” When therecording is stopped, a study code or patient identifier can be added tothe file.

Referring back to FIG. 1, a collector device 104 is coupled to thesensors 102 for pre-processing the real-time raw sensor data orbrainwave signal data. The sensors 102 provide raw sensor data (e.g. rawEEG data) to the collector device 104. The collector device 104 isconfigured to pre-process the sensor data, such as by filtering outnoise, to generate filtered brainwave signal data. As further examples,the collector device 104 may implement pre-processing for artifactreduction, reduction of volume conduction and reference electroderemoval, for example. The collector device 104 connects to a serversystem 100 via network 150 to transmit the brainwave data collected fromsensors 102. In some example embodiments, server system 100 may bedirectly connected to sensors 102 to directly receive the raw sensordata or brainwave signal data to provide a stand-alone solution.

The server system 100 is configured to process the real-time raw sensordata or brainwave signal data to compute a connectivity matrix for brainentropy, a real-time brain value index (BVI or Functionality Index (FI)and treatment guidance. The brain value index can also be referred to asa functionality index or brain viability index. The brain value indexcan correspond to a real-time brain state of a patient.

The server system 100 is configured to compute, using the sensor data, aconnectivity matrix having connectivity values, a connectivity value foreach pair of channels. The server system 100 is configured to generatevisual elements in real-time that represent the real-time brain valueindex to depict the brain state of the patient and a connectivity mapfor the connectivity matrix. The connectivity map visually depicts thechannels and a connecting line between a pair of channels representingthe strength of connection between the pair of channels. Theconnectivity map may show multiple connecting lines between channelpairs. The brain state can indicate different anxiety states ordisorders, for example.

Sensors 102 can include different types of sensors to capture differentbiological and brainwave data. Sensors 102 correspond to differentchannels. The server system 100 processes the sensor data usingprocessing rules to detect patterns and evaluate cortical andsubcortical activity in conscious and unconscious states. The serversystem 100 computes a connectivity matrix for brain entropy to evaluatethe number of “connections” between areas of the brain and theassociated entropy and complexity. Conscious states may result fromhigher entropy and complexity that are dependent on the number ofconfigurations of connected pairwise combinations computed from the rawsignals. The number of pairwise channel connection combinations sets alimit on the number of possible configurations.

As channels may be connected or not connected, entropy and complexitycan be maximized when the number of connected channel configurations isequal to half of all possible channel configurations. Entropy occurswhen the individual is processing sensory inputs in a normal manner(e.g. awake with open eyes). Half of the number of configurations ofinteractions may represent the most probable distribution of energy andis associated with conscious awareness. These results encapsulate threemain theories of cognition: the metastability of brain states, theglobal workspace theory and the information integrated theory.Consciousness may represent thus an optimal channel for accessingsources of free energy and is an emergent property of the distributionof energy (information) in the nervous system.

In the case of anxiety, while the patient is conscious, he or she is notfully functional as seen in the physiological changes (increased heartrate, gastrointestinal upset, sweating, increased respiratory rate).Thus the entropy and connectivity are lower than in the non-anxious,conscious state.

In some embodiments, the server 100 computes a connectivity matrix fromthe brainwave data. The connectivity matrix is used by the server system100 in order to compute the BVI values. In some embodiments, the server100 computes phase synchronization for each channel pair angle. Theentries of the connectivity matrix are values for each pair combination.The server 100 can use the entries to generate a visual connecting linebetween channel pairs in the connectivity map.

Connectivity is a function of phase synchrony values computed by server100, also known as the R index, and can be an integer between 0 and 1,for example. Phase synchrony (synchronization) evaluates theconnectivity between 2 oscillating signals, such as the EEG waveformoutput from 2 channels. It is an integer value between 0 and 1. Server100 can calculate phase synchrony using the Hilbert as follows: R=|

e^((iΔθ))

| where Δθ is the phase difference (or angle) between two signals. Thevalue can be dependent on the length of time specified for thecalculation (1 second running window for our device) and the frequencyof the signal (3 Hz for our device).

Server 100 can generate a connectivity map for an interface of a displaythat indicates the channels and connecting lines between channel pairsbased on the strength of connection between a respective channel pair.

Referring back to FIG. 11A there is shown a connectivity map 1106 aspart of interface 1100. The connectivity map 1106 visually depicts anarrangement of channels. In some embodiments, server system 100 isconfigured to generate connecting lines between channels of theconnectivity map 1106 to indicate the strength of the connection betweenchannels. For example, a lighter line can indicate a weaker connection(e.g. lower connectivity value) than a darker line. The connectivity mapvalues may be computed using the sensor data. Accordingly, theconnecting line changes visually depending on the strength of theconnection. The interface 1100 can also include a listing ofconnectivity map values. Each channel pair can have a correspondingconnectivity map value. A list of values may be displayed as visualelements 1108 and may range between 0 and 1. The raw EEG data may alsobe displayed for each channel.

For example, the connectivity map 1106 can be a graphical depiction ofthe 8 electrodes (channels) representing the strength of theconnectivity (e.g. phase synchronization) between each of the possiblechannel pairs. The threshold of a phase synchrony value can be 0.45, forexample. The threshold can be calculated by server 100 using sensor datafrom normal adult subjects in the awake state with eyes open. Fourlevels of connectivity strength are defined by the following ranges andillustrated with connecting lines of different colours and thicknesses.The example connecting lines are shown as light grey and 1 pointthickness defines connectivity between a pair of electrodes with a phasesynchrony value of 0.45 to <0.6; Medium grey and 1.5 point thickness fora phase synchrony value of 0.6 to <0.8; Dark grey and 2 point thicknessfor a phase synchrony value of 0.8 to <0.9; and black with a 3 pointthickness for a phase synchrony value of 0.9 to 1.0<0.6. This is anexample visual representation.

The server 100 computes the connectivity matrix by calculating entropyfrom the phase synchrony values for each electrode pair. The server 100can also compute a boolean matrix such that each electrode pair's phasesynchrony is compared to the threshold (0.45) and assigned a “0” if itis below the threshold or “1” if it is above the threshold. Thisthreshold generates a simplified view of the complex data while stillgiving clinically useful discernible output.

In some embodiments, the server computes the connectivity matrix as aboolean connectivity matrix where the entries are, 0 if a correspondingindex is lower than a threshold, and 1 if higher, where the servercomputes a threshold from the average of indices of normal adults witheyes open, where connected channels are defined as entries 1. Thethreshold of the phase synchronization (R) can be generated fromaveraging the mean phase synchrony value for control subjects at 3 Hzover 10 second epochs in the alert state with eyes open.

The server system 100 calculates a Brain Value Index (BVI) orFunctionality Index or “normalized entropy” using connectivity valuesfor the channel pairs of the total number of possible connections. Phasesynchronization is calculated for each pair of channels and a“connectivity” matrix S is obtained, whose entries are the averagevalues of the synchrony index for each pair combination. From this one,a boolean connectivity matrix B is calculated, with 0 entry if thecorresponding synchrony index is lower than a threshold, and 1 ifhigher. We define two channels “connected” if the corresponding entry inmatrix B is 1. Then we use the combinations of connected channels as a‘complexity’ measure. The total number of possible pairs of channelsgiven a specific channel montage is N=Nc!/2!(Nc−2)! where Nc is thetotal number of channels in the recording system, normally 144-146 incase of MEG sensors, between 19 and 28 in case of scalp EEG and 8channels in an example prototype. The channel numbers are specified,below, in each case. For instance, in example MEG recordings there maybe Nc=144, thus N=10296 possible pairs of connected sensors. For eachsubject server system 100 calculates p (the number of connected pairs ofchannels) in the different behavioural stages, using the threshold ofthe synchrony index of 0.45 based on the average phase synchronizationof normal adults in the alert state with eyes open, and estimate thenumber of possible combinations of those p pairs, C, using the binomialcoefficient again: C=N!/p!(N−p)!. These calculations represent thecombinatorial problem: given a maximum total of N pairs of connectedsignals, in how many ways our experimental observation of p connectedpairs (that is, the number of l′s in matrix B) can be arranged. Theentropy and Lempel-Ziv complexity associated with those p values arethen computed by server 100. In the final step, each entropy value isdivided by the maximal entropy value and then multiplied by 100. Anormal brain needs to synchronize (measured by the phase synchronyvalues). If the brain is too connected then it may be over excited andif not connected at all then may be non-responsive.

The possible values for the Brain Value Index are constrained by thenumber of channels and configuration of electrodes. In the 8 channelexample prototype there can be 14 different values of the brain valueindex (rounded to whole numbers): 22, 37, 49, 59, 67, 74, 81, 84, 90,94, 96, 98, 99, 100. There are 28 possible connections or channel pairs.There may be half the number of BVI values, or 14. For the example with8 channels, the normalized entropy can be 1 of 14 possible values oneither side of the curve. A further possible value is the maximumentropy value (centre of the curve), which is also a unique value. Thatis, there are 14 other unique values (in addition to the maximum entropyvalue) on each side of the centre point. Accordingly, for this example,there can be 29 total values. There is also a 0 value when there are 0connections.

The BVI values are based on the total number of possible connections fora given number of channels. In the example with 8 channels there are 28possible connections (plus 1 for the ‘no connection case’). For each‘number of connections’ at a given point in time, server 100 cancalculate the Brain Value Index, which only depends on the number ofconnections, so this is why there are only 15 possible normalizedentropy values in this example since the curve is symmetric.

An approximation for entropy S or Brain Value Index can be representedas:

Entropy(N)=C*log(C/(C−N))−N*log(N/(C−N))

where

C=the maximum number of total pairs of connections

N=the number of active connections

log=the natural logarithm

The normalized entropy can be represented as:

normalizedEntropy=100*Entropy/maxEntropy

where maxEntropy is:

maxEntropy=Entropy(C/2)

or

maxEntropy=C*log(C/(C−(C/2)))−(C/2)*log((C/2)/(C−(C/2)))

A different number of electrodes can generate different set of possiblevalues for the Brain Value Index. For example, they can be intermediatevalues on the curve.

The server system 100 has a display controller to issue control commandsto continuously update an interface at a display device 106 inreal-time. The display device 106 has an interface to generate andupdate a visual representation of the real-time brain value index andthe treatment guidance based on the issued control commands from theserver system 100. In some example embodiments, the collector device 104and server system 100 or may couple to display device 106 to controlrendering on display device 106 and provide visualizations of thebrainwave data from the sensors 102, brain value index and connectivitymatrix. Feedback data received in response to the display on displaydevice 106 of the visualizations of the brainwave data from the sensors102 may also be used to refine collector device 104 processes, forexample.

The server system 100 processes the brainwave data for real-time brainmonitoring. The server system 100 connects to display device 106 tocontrol rendering on display device 106 and provide visualizations ofdata in real-time as interface elements of an interface. Feedback datamay be received at display device 106 which may be used for machinelearning or training to refine server system 100 processing rules, forexample. The server system 100 may be remote or local to othercomponents to provide remote input, remote monitoring or remote viewingin various embodiments. The server system 100 may integrate anonymizedsensor data from other patients with similar treatments or conditionsfor machine learning and benchmarking. The server system 100 mayintegrate historical data for the patient for machine learning andbenchmarking. In some embodiments, the server system 100 can access acloud storage device that correlates patient data.

The server system 100 is configured for real-time brain monitoring andgenerates output data to update an interface on a display device 106with interface elements to provide visual representations of the outputdata and a treatment guide for the patient. Accordingly, the serversystem 100 provides discernible effects at least at the interface ofdisplay device 106. For example, the treatment guide can indicate.

Graphical Display

The server system 100 processes the brainwave data to identify or detectfeatures or patterns of optimal (or suboptimal) brain organization thatallows for adequate processing of sensory stimuli and that may guide theemergence of cognition and consciousness. The server system 100processes the brainwave data to identify or detect indicators ofconscious and unconscious states of a patient's brain. As an example,normal wakeful states may be characterised by greater number of possibleconfigurations of interactions within a patient's brain network. Thegreater number of interactions within a patient's brain network(information exchange) can represent highest entropy values and thebrainwave data can indicate a probable distribution of information andenergy. The server system 100 processes the brainwave data to identifyor detect interactions within the brain network or lack thereof.

Consciousness arises from the organization of matter and may beconsidered an emergent property of the brain organization.Neurophysiologic recordings of brain activity (e.g. brainwave signalscaptured by EEG sensors) can show persistent fluctuating patterns ofcellular interactions within a patient's brain network. This variabilityin fluctuating patterns of cellular interactions indicates a range ofbrain states. A brain has different configurations of connections ofwidely distributed networks that exchange information, and support theflexibility needed to process sensory inputs and cognition. Fluctuationsin brain coordinated activity and metastable dynamics may be captured byEEG sensors as brainwave signals and used clinically to evaluate brainfunction. There may be certain general organization of cell ensemblesthat may be optimal for processing of sensory inputs (i.e. consciousawareness). An organising principle is the tendency toward maximal ormore probable distribution of energy/matter. Brain organization may be amanifestation of the tendency towards a widespread distribution ofenergy or maximal information exchange. The server system 100 processesthe brainwave data to implement real-time brain monitoring to evaluateand understand brain function and the interactions within the brainnetwork.

The server system 100 captures brain waves signals using sensors 102.The server system 100 computes a (near) real-time brain value index todetermine and evaluate a brain state. The server system 100 definesboundaries or ranges of values for the brain value index in order todefine different brain states. That is, a particular brain state isassociated with a range of values for the brain value index.

The server system 100 implements real-time brain monitoring byprocessing the brainwave signals captured by sensors 102 to compute thebrain value index as an assessment of the patient's brain state.

In some embodiments, the server system 100 monitors brain function usingsensors 102 and generates an interface on display device 106 to providea visual representation of treatment guidance and an indicator for thereal-time, changing brain value index. The server system 100 controlsand updates the interface on display device 106 in real-time to updatethe visual representation of the brain value index and treatmentguidance. The treatment guidance may include an indication orrecommendation to continue the current treatment (monitor mode) orre-evaluate the patient and adjust treatment (intervention mode). Theseare illustrative example treatment guides and visual representations forthe interface. The server system 100 integrates the brainwave data withother biological data such as brain and heart variability measures (e.g.received from sensors 102) with machine learning rules to provideindividualized patient monitoring using the real-time, changing brainvalue index and treatment guidance. The interface on display device 106may provide a graphical display of treatment guidance and the real-timebrain value index for a patient may be self-referential with real-timeupdates.

In some embodiments, the server system 100 implements machine learningto compute the brain value index based on historical data for thepatient or other patients. In a first step, a classifier algorithm iscreated. Each patient has a series of Functionality Index values and aknown outcome (e.g. A dichotomous outcome of panic or no panic). Theoutput from the training set is used with a test set of new patient datain order to identify the anxiety state. Patient outcome based on newrecordings would be predicted based on accumulation of FunctionalityIndex values.

The interface provides a real-time indication of different brain statesdetermined based on the real-time, changing brain value index computedby processing brainwave signals, along with treatment guidance for thedifferent brain states. The interface can provide an indication of thebrain value index using a graph representing ranges of brain functionand with an indicator along the graph representing the real-time brainvalue index.

In some embodiments, the server system 100 generates treatment guidancefor display at interface of display device 106. For example, thetreatment guidance can provide an indication of a monitoring state foranxiety and an intervention state (prescribed medication,self-medication, relaxation techniques and evaluation of the treatmentefficacy in relation to the computed brain value index. In someembodiments, the display device 106 provides feedback data to refine orupdate the processing by the server system 100.

In some embodiments, the server system 100 implements machine learningto generate recommended treatments as part of the treatment guidancebased on historical data for the patient or other patients. In someembodiments, the server system 100 may generate recommendations as partof the treatment guidelines based on historical data for the patient orother patients in similar conditions. For example, there may have been arecent successful treatment of a patient with a particular brain stateusing a specific treatment process that can be recommended to anotherpatient with a similar brain state detected using the real-time brainvalue index. The server system 100 may continue the real-time brainmonitoring using the sensors 102 during treatment to assess the patientresponse to treatment. This assessment may be used to refine or generatetreatment recommendations for the patient or other patients with similarbrain states.

The display device 106 may be remote from the location of the patient toenable remote monitoring of the real-time brain state of the patient,such as in the community or geographically remote and underservicedregions. The display device 106 may also be local to the patient orthere may be both a remote display device 106 and local display device106. For example, the server system 100 may generate an alert to callfor a secondary opinion to review and monitor the patient by anadditional remote display device 106. As another example, server system100 may use a remote display device 106 that automatically generates anddisplays alerts in response to detecting building anxiety. The serversystem 100 may transmit alert notifications for the generated alerts.

FIG. 2 shows another example system for real-time brain monitoring.Server system 100 may include a network interface 222 to receive sensordata (e.g. brainwave data) from sensors 102 over network 250. As anillustrative example, server system 100 may couple to multiple sets ofsensors 102 for real-time brain monitoring of multiple patients.

Acquisition unit 230 receives raw sensor data from sensors 102. In someembodiments, acquisition unit 230 receives raw sensor data (includingbrainwave data, EEG data) data from sensors 102 in real-time or nearreal-time. Acquisition unit 230 saves acquired sensor data into the datafile. The sensor data can be time coded and linked to a patientidentifier. In simulation mode, acquisition unit 230 is configured toplay back acquired EEG data from data file as a visual representation ofthe EEG data on display device 106. The acquisition unit 230 isconfigured to play back sensor data acquired from different sensors 102from tab delimited data files.

Processing unit 232 interacts with phase synchronization unit 218 andindex unit 220 to transform raw sensor data to generate and updateconnectivity matrix data (and connectivity map) and brain value indexdata. For example, the screenshot of interface 700 shown in FIG. 7 caninclude The Connectivity Map Values 708 (extreme left hand side panel),show the phase synchrony value for each of the 28 channel pairs as lineswith different point thickness. The Functionality Index is thencalculated as previously described, with each channel pair evaluated asbeing below (assigned “0”) or above (assigned “1”) the threshold.

Presentation unit 234 generates visual representations of the brainvalue index, sensor data, and connectivity matrix on interface ofdisplay device 106. Presentation unit 234 processes control commands toupdate the visual representations and control sensors 102 for capturingbrainwave data. For example, presentation unit 234 interacts withdisplay device 106 or sensors 102 to implement device control commands(e.g. start/stop) and determine device statuses. Presentation unit 234generates visual representations for raw EEG data visualization,connectivity map visualization, and brain value index visualization oninterface of display device 106.

Each set of sensor data from sensors 102 may be tagged with a patientidentifier to distinguish between sensor data captured from differentpatients. The sensor data from sensors 102 may be tagged with a timeidentifier (e.g. time codes) to distinguish between sessions of sensordata from the same patient. In some example embodiments, sensors 102 mayprovide data directly to server system 100. In some example embodiments,sensors 102 may provide data indirectly to server system 100 viacollector device 104. Collector device 104 may couple to one or moresets of sensors 102 for pre-processing of the raw sensor data andprovide the pre-processed sensor data or brainwave data to the serversystem 100. Collector device 104 may couple to a local, external datastorage device 212 to store the pre-processed sensor data or brainwavedata. Display device 106 may couple to sensors 102, collector device 104and server system 100 to display visual representations of the rawsensor data, pre-processed sensor data, or brain value index data forthe real-time brain monitoring and treatment guidance as part of agraphical user interface of display device 106, for example.

Server system 100 may also couple to central data storage device 216 toprovide data for the real-time brain monitoring and receive otheraggregated brainwave data (from e.g. cloud server) for machine learningand refinement of the process for real-time brain monitoring. Forexample, central data storage device 216 may provide a data repositoryof historical brainwave data collected from the same patient or otherpatients which may be used as part of the process for real-time brainmonitoring. The central data storage device 216 may also store rawsensor data (from sensors 102, 202) and pre-processed sensor data (fromcollector device 104) to provide a central repository of all data forsystem 100.

A translation unit 214 may implement translation, re-formatting orprocessing of raw sensor data (from sensors 102, 202) and pre-processedsensor data (from collector device 104) for storage. The central datastorage device 216 may serve one health care facility or multiple healthcare facilities and may receive data from multiple server systems 100,sensors 102 and collector devices 104. The central data storage device216 may provide a big data platform for machine learning and correlationdetection for treatment guidance. The central data storage device 216may provide data storage for review of individual patient trajectories.In some example embodiments, the central data storage device 216 mayprovide data storage for multiple patients. The central data storagedevice 216 may implement big data processing using k-means clusteringand related classification techniques and state space representation.

Server system 100 may also include various functional hardwarecomponents for real-time brain monitoring. For example, server system100 may include a phase synchronization unit 218 configured to calculatea connectivity matrix and an index unit 220 configured to compute thereal-time, brain state index and treatment guidance as described herein.The server system 100 may also include local memory or data storagedevice 224. The network interface 222 may transmit control commands todisplay device 106 to generate and update its interface. The networkinterface 22 may also transmit control commands to actuate treatmentrelated machines to trigger treatment or intervention for patient basedon the computed brain state index.

FIG. 3 shows a flow chart of a process for real-time brain monitoring.

At 302, the server system 100 may trigger a real-time brain monitoringsession initialization process. The initialization process may involvecalibration of sensors 102, 202 and collector device 104. For theinitialization process, the server system 100 may calculate allvariables that do not change throughout the session. Specifically, thissession initialization step is performed to optimize real-time signalprocessing by pre-computing otherwise redundant, ongoing computations.This step also sets up all local memory and resource allocation forreal-time signal processing.

At 304, the server system 100 may trigger a sensor acquisition process(e.g. EEG or brainwave data acquisition) to acquire data from sensors102 or collector device 104. The sensor acquisition process may beimplemented by a combination of one or more of sensors 102 collectordevice 104 and server system 100. As an illustrative example embodiment,sensors 102 may include a wearable device or headset with eight totwelve dry electrodes to acquire raw sensor data from a patient alongwith one or more electrodes to acquire reference data. For an exampleprototype design, in addition to ease of electrode application, thesensor placement captures data from brain regions that provide importantinformation on normal function and pathology. At minimum, the 8electrodes capture data from the frontal lobes (F7 and F8); anteriortemporal (T3 and T4) which include memory regions, posterior temporal(T5 and T6) which includes part of the parietal lobe that integratesinformation and the occipital region (O1 and O2) that contains thevisual cortex. This is an example montage. The sensors 102 may includeelectrodes to capture EEG data and the sensor acquisition process mayinvolve EEG analog signal acquisition from the headset. Further, thesensor acquisition process may involve EEG analog signalpre-amplification in headset and EEG analog to digital signalconversion. The sensor acquisition process may involve transfer of EEGdigital signal to collector device 104 and server system 100 forprocessing. For example, the sensors 102, 202 may be wireless,non-contact EEG and EKG electrodes.

In some embodiments, sensors 102 include electrocardiogram (EKG) sensorsto capture EKG data. Acquisition of EEG and EKG data can be followed bycalculating BVI values and heart rate or blood pressure using the signaldata of each signal respectively. The relationship between the twovalues are represented in state space, where a graph of optimalphysiological functioning is seen in FIG. 10. Using 2 physiologicalindicators, the relationship between the Brain Value Index (y axis) andthe heart rate entropy (x axis). In the nonanxiety state, theFunctionality Index and heart rate values would expected to be variablerepresenting the baseline state. A patient with anxiety would experiencea change in the values and a decrease in the variability of 1 or bothvalues.

The state space reconstruction in the above graphs depict therelationship between the FI—Functionality Index (y axis) and heart rate(x axis) for an equivalent 65 second time period for baselinefunctioning (top panel) and during the panic event (bottom panel). Thevariance of the heart rate is statistically significantly differentbetween the baseline state (15.5) and the panic state (2.5).

In FIG. 3, at 306, the server system 100 may implement real-time signalprocessing. In example embodiments, the real-time sensor processing maybe implemented by a combination of one or more of sensors 102, 202,collector device 104 and server system 100. Real-time processing may beachieved by implementing data analysis processes in a high-performanceprogramming language such as C, C++, or Java. Other techniques toimprove real-time processing speeds include the session initializationstep 302.

As noted, collector device 104 may pre-process the raw brainwave signaldata for noise filtering, artifact reduction, reduction of volumeconduction and reference electrode removal, for example. In otherembodiments, sensors 102 may integrate with hardware chip on headsets toimplement pre-processing on acquisition of the raw signal data. Infurther embodiments, the server system 100 may pre-process the rawsensor data instead of or in addition to collector device 104. Pleaseprovide any further details on the preprocessing for the brainwavesignals

Server system 100 may process the brainwave signal data to generate aconnectivity matrix. Server system 100 may define a time period of asliding window. As an illustrative example, the server system 100 maydefine a 1 second sliding window. The server system 100 (e.g. phasesynchronization unit 218) may implement a Hilbert transform to calculatethe instantaneous angle of a channel. This may be followed by a phasesynchrony calculation (R) for each instantaneous angle channel. Theserver system 100 computes a connectivity matrix (S) (entries are the Rvalues for each pair combination) used to generate the brain stateindex. As an illustrative example, server system 100 may calculate aBoolean connectivity matrix (B) where the entries are, 0 if thecorresponding R index is lower than an R threshold, and 1 if higher. Theserver system 100 may calculate a threshold from the average of Rindices of normal adults with eyes open. Connected channels may bedefined as entries of B=1. It can be helpful to include a few examplesof the connectivity matrix

The server system 100 (e.g. index unit 220) computes a Brain Value Indexcalculation (BVI) or Functionality Index or “normalized entropy” using atotal number of possible connections. Phase synchronization iscalculated for each pair of channels and a “connectivity” matrix S isobtained, whose entries are the average values of the synchrony indexfor each pair combination. From this one, a Boolean connectivity matrixB is calculated, with 0 entry if the corresponding synchrony index islower than a threshold, and 1 if higher. We define two channels“connected” if the corresponding entry in matrix B is 1. Then we use thecombinations of connected channels as a ‘complexity’ measure. The totalnumber of possible pairs of channels given a specific channel montage isN=Nc!/2!(Nc−2)! where Nc is the total number of channels in therecording system, normally 144-146 in case of MEG sensors, between 19and 28 in case of scalp EEG and 8 channels in our prototype. The channelnumbers are specified, below, in each case. For instance, in our MEGrecordings we have Nc=144, thus N=10296 possible pairs of connectedsensors are obtained. For each subject we calculate p (the number ofconnected pairs of channels) in the different behavioural stages, usingthe threshold of the synchrony index of 0.45 based on the average phasesynchronization of normal adults in the alert state with eyes open, andestimate the number of possible combinations of those p pairs, C, usingthe binomial coefficient again: C=N!/p!(N−p)! All these calculationsrepresent the relatively simple combinatorial problem we are trying tosolve: given a maximum total of N pairs of connected signals, in howmany ways our experimental observation of p connected pairs (that is,the number of 1's in matrix B) can be arranged. The entropy andLempel-Ziv complexity associated with those p values are then computed.In the final step, each entropy value is divided by the maximal entropyvalue and then multiplied by 100. In the example 8 channel prototypethere are 14 possible values of the Brain Value Index (rounded to wholenumbers): 22, 37, 49, 59, 67, 74, 81, 84, 90, 94, 96, 98, 99, 100.

At 308, the server system 100 (e.g. index unit 220) computes output datato control the display device 106 to update the interface with interfaceelements to provide a visual representation of the output data. Theserver system 100 continuously transmits the BVI for real-timemonitoring using a controlled graphical display.

At 310, the server system 100 may receive feedback from display device106 or other computing device to refine the processing to createindividual thresholds or population based thresholds. At 312, the serversystem 100 uploads the data to one or more storage platforms (e.g.central data storage device 216, local data storage device 224, externaldata storage device 212).

FIG. 4 shows an example interface 400 with interface elements to providea visual representation of real-time brain state monitoring according tosome embodiments. As shown in the graph curve, a marker 402 reflectiveof the real-time brain value index (brain state) may move along thecurve in real-time as a visual representation of the processed brainwavesignals. For different brain states, the server system 100 may definewith preset alarm threshold values or ranges for the processed brainwavesignals for the patient. The level of anxiety may correspond todifferent treatment guides, such as deep breathing or relaxationtechniques (Functionality Index 75-86), or medication treatment(Functionality Index <75). If the real-time processed brainwave signalchanges this may trigger the marker 402 to move along the curve andtrigger an alert or intervention such as treatment.

Control indicia 406 on interface 400 (or a separate control device) maytrigger treatment to patient such as by triggering stimulation to thebrain of the patient. The interface 400 may overlay historical dataabout the patient or another patient with a similar condition asadditional visual representations. Transmission indicia 408 on interface400 (or a separate device) may trigger transmission of feedback dataregarding the patient which may be used to refine the processing rulesfor machine learning. FIG. 4 provides an illustrative example, where theinverted U-shape curve 410 with a sliding marker 402 provides a visualrepresentation which represents the normalized entropy (FunctionalityIndex, Brain Value Index) value of 0 to 100 (y-axis) versus the numberof possible channel pair combinations (x-axis). With the 8 channels ofour prototype device, the x axis will be 28 possible channel pairs. Thenormalized entropy calculation has been described and the value reflectsthe amount of information processing by the cortical networks of thebrain, where 100 is the maximum information processed by a conscious,normally functioning adult brain. This curve may be calculated on asession-by-session basis (at the session initialization 302, dependenton the number of electrodes) and may be constant throughout thatsession. The marker 402 represents the real-time normalized entropy andreal-time number connected pairs of channels as the real-time brainstate index. As an example, the sliding marker 402 can be in 1 of 3zones: (1) top of the curve—good, direct staff to monitor maintaincurrent treatment, (2) mid slope—review patient and re-evaluatetreatment, (3) bottom of curve—urgent intervention or if patient isbeing monitored as an organ donor, activate the organ retrievalprotocol.

The following provides further illustrative example processes forelectrophysiological recordings and real-time brain state monitoring.The acquisition rate or sampling frequency may vary from 200 to 625 Hz.The sampling frequency is addressed in the algorithm used to calculatethe phase synchrony. The prototype device has a default setting of 500Hz acquisition rate. The duration of the recordings may vary from 2minutes to 55 minutes. The sleep data may be 2-4 minutes in length. Thisis an example experiment for illustration only.

The pre-processed data may be from sensors 102 on the scalp EEGs whichmay be processed using a Laplacian to avoid the potential effects of thereference electrode on synchronization, using the current source density(CSD) algorithm. The reference electrode may be placed on the scalp oron one or both ears (linked ears) may be used. The prototype deviceemploys the right ear as the standard reference electrode. Analysis mayinvolve computing the phase synchrony index (e.g. brain value index) byestimating phase differences between two signals from the instantaneousphases extracted using the analytic signal concept via the Hilberttransform. Several central frequencies, ranging from 3 to 30 Hz may bechosen with a bandpass of 2 Hz on either side. In the prototype devicethe default setting is 3 Hz. The 3 Hz is in the delta bandwidth and withthe ±2 Hz range, encompasses 1 Hz at the lower end, which is the onlyfrequency generated by the cortex to 5 Hz at the upper end which is inthe theta range. The phase synchrony index (R) may be calculated using a1-second running window, obtained from the phase differences using themean phase coherence statistic which is a measure of phase locking andis defined as R=|

e^((iΔθ))

| where Δθ is the phase difference between two signals.

Phase synchronization is calculated for each pair of channels and aconnectivity matrix S is obtained, whose entries are the average valuesof the synchrony index for each pair combination. A Boolean connectivitymatrix B is calculated, with 0 entry if the corresponding synchronyindex is lower than a threshold, and 1 if higher. The threshold has beenpreviously defined as 0.45 based on the mean phase synchronization valueat 3 Hz of normal control subjects in the awake state with eyes open.Two channels may be “connected” if the corresponding entry in matrix Bis 1. The combinations of connected channels may provide a ‘complexity’measure. With p=2 as an example, the total number of possibleconnections given a specific channel montage is N=Nc!/2!(Nc−2)! where Ncis the total number of channels in an example recording system. This maybe 144-146 in case of MEG sensors 102, and between 19 and 28 in case ofscalp EEG sensors 102. The channel numbers are specified, below, in eachcase. For instance, in MEG recordings we have Nc=144, thus N=10296possible pairs of connected sensors 102 are obtained. For each subjectwe calculate p (the number of connected pairs of channels) in thedifferent behavioural stages, using the threshold of the synchrony index(which varies for each subject, but whose average is 0.45) methodaforementioned. The server system 100 estimates the number of possiblecombinations of those p pairs, C, using the binomial coefficient again:C=N!/p!(N−p)! All these calculations represent the combinatorial problemwe are trying to solve: given a maximum total of N pairs of connectedsignals, in how many ways our experimental observation of p connectedpairs (that is, the number of 1's in matrix B) can be arranged. We thencompute the entropy and the Lempel-Ziv complexity associated with thosep values.

It must be noted that, while the words synchrony and connectivity may beused synonymously, in reality phase synchrony analysis reveals acorrelation between the phases of the oscillations between two signals.Connectivity depends on several other factors:

-   -   I) Length of epoch. The longer the time epoch is that is being        analyzed, generally the lower the phase synchrony value. Two        electrodes may have a very high phase synchrony index        (e.g. >0.9) for 1 to 2 seconds as in the case of the patient        with absence epilepsy, during the seizure event. Connectivity in        the same electrode pair in the same patient may show a phase        synchrony value of 0.6 over 10 seconds if non seizure events are        included. Given that neuronal and network connections in the        brain occur at the millisecond time scale, high phase synchrony        values for 10 seconds would be considered pathological and seen        in prolonged seizure events.    -   II) Channel connectivity versus whole brain connectivity. Phase        synchrony is always calculated between 2 electrodes for the        specified time epoch. A channel pair (e.g. T3 and T4) may have a        high phase synchrony value (>0.9). If hypothetically, this is        the only channel pair out of the possible pairs from the 8        electrodes [8!/(2!×6!)=28] that shows any connectivity, then        both the channel pair connectivity and the whole brain        connectivity are the same.    -   III) Frequency of interest. The prototype analyzes the 3 Hz        bandwidth, ±2 Hz. If the same algorithm used to calculate phase        synchrony at 3 Hz is used to calculate that at 15 Hz, without        altering the algorithm, the resultant value will be falsely        lowered.

In some example embodiments, phase relations may represent, at least,some aspect of a functional connectivity. Hence, in order to evaluateinteractions (“connections”), server system 100 may take each sensor 102as one “unit”, and define a pair of sensors 102 (signals) “connected” ifthe phase synchrony index is larger than a threshold. The threshold isdetermined by server system 100 for each individual, and is the averagesynchrony index in the ‘awake eyes-open’ condition, when the individualis alert and processing the sensorium in a regular fashion. The data mayinclude MEG, scalp EEG, intracerebral recordings, or other types ofrecordings. While there may be reference to signal level processing, theMEG and scalp EEG sensors 102 record cortical activity and thusthroughout the text the terms brainwave signals or brain areas/networksmay be synonymous. Server system 100 may consider the global states inaddition to the specific pattern of connectivity among brain sources.

The server system 100 may consider features of brain organization thatallow for sufficient sensory stimuli processing to support theconscious, awake state. The greater number of possible configurations ofinteractions between brain networks is associated with alert states, andrepresented high entropy. In contrast, lower entropy associated withfewer combinations of connections, is characteristic of eitherunconscious states or fewer input states (eyes closed, anxiety). Thisobservation reflects a general organising principle. The emergentproperty of this collective level of description is that consciousnessis a manifestation of the second principle of thermodynamics. The secondprinciple of thermodynamics states that in isolated systems, entropynever decreases; that is, the system will approach equilibrium withmaximum entropy. However, in systems that exchange matter/energy, likethe brain in its activity, the S may decrease. Nevertheless consideringthe whole system, the non-isolated plus the environment, the S stillwill never decrease. The brain is an open system and thus what weobserve is that, while it tends to reach equilibrium with max S, itremains close to it (in fully alert states) but does not achievecomplete equilibrium because of the exchanges of energy/matter with thesurroundings (e.g. heat loss from metabolism). Also, in statisticalthermodynamics, entropy is a measure of the number of microscopicconfigurations that a thermodynamic system can have when in a state asspecified by certain macroscopic variables. When evaluating entropy inthe brain, entropy can be seen as the information content in thefunctional network. The state of alertness in the human brain can beseen as the condition in which there is maximal information within thefunctioning networks that give rise to the conscious state. Maximalinformation is thus maximal entropy. In our brain monitoring system,this maximal entropy value is reflected in the Functionality Index orBrain Value Index of 100 at the top of the curve. The concept ofinformation being equivalent to entropy is in the Shannon definition ofentropy which is equivalent to the Boltzmann/Gibbs definition inthermodynamics and there are similarities in the equations that defineboth information and entropy.

With the advent of ‘Big Data’ and the related torrent of empiricalobservations emphasising the exhaustive scrutiny of elementarybiological processes, the search for organising principles that resultin the emergence of biological phenomena seems more crucial than everlest we drown in the flood of data. The server system 100 processing maycapture the bounds in the global organization of a biological system tobecome adaptable (i.e., respond) to an environment, or, inneuroscientific terms, features of optimal brain organization (in termsof connections) that allows brains to adequately process sensorystimuli. The server system 100 may focus on the global states, and insome instances, additionally on specific patterns of connectivitybetween brain areas. The term ‘connectivity’, may refer to a correlationbetween phases of oscillation.

The server system 100 may consider that the number of pairwise channelcombinations—that is, its interactions/connections between brainnetworks—occurs near the maximum of possible configurations in periodswith normal alertness. This may indicate that the greater number ofconfigurations of interactions represents the most probable distributionof energy/information resulting in conscious awareness. In the finalanalysis, information exchange implies energy exchange, hence weinterpret Information exchange as energy redistribution.

Aspects of awareness emerge when certain levels of complexity arereached, it is then possible that the organization (complexity) neededfor consciousness to arise needs the maximum number of configurationsthat allow for more variety of interactions between cell ensemblesbecause this structure leads to optimal segregation and integration ofinformation, two fundamental aspects of brain information processing.

Microstates that yield the same macrostate form an ensemble. Hence, themacrostate with higher entropy as defined, is composed of manymicrostates (the possible combinations of connections between diversenetworks), and can be thought of as an ensemble characterised by thelargest number of configurations. In neurophysiological terms, eachmicrostate represents a different connectivity pattern and thus isassociated with, in principle, different behaviours or cognitiveprocesses. The macrostate that we find associated with wakeful normalstates (e.g. eyes open) is the most probable because it has the largestentropy (largest number of combinations of connections). Hence optimalinformation processing seems to be the result of the most probabledistribution of energy (information) among brain networks. At the sametime, the ensemble of microstates associated with normal sensoryprocessing features the most varied configurations and therefore offersthe variability needed to optimally process sensory inputs. For themetastability of brain states, the states should not be too stable forefficient information processing, hence the larger the number ofpossible interactions, the more variability is possible. Equally, theresults are consistent with the global workspace theory in that the mostwidespread distribution of information, the more optimal its processing.Finally, these observations relate as well to the information integratedtheory, in that consciousness increases in proportion to the system'srepertoire of states, thus the more combinations possible, the morestates, and here we can define states as configuration of interactions.

Additionally, the results support computational and theoretical studiesshowing that patterns of organised activity arise from the maximizationof fluctuations in synchrony and by just varying the probability ofconnections in neural networks, and in general highlight all proposalsof the fundamental importance of fluctuations in nerve activity as thesource of healthy brain dynamics. FIG. 7 shows an example interface witha visual representation providing an interface 700 of brain connectivity(left) for different example EEG channels, a graph 702 with a marker 704for the real-time brain value index (right top) or normalized entropy,and interface elements 716 for raw EEG signals per channel (rightbottom). A list of calculated brain value index values 708 is visuallyrepresented. Stronger connections between channel pairs can berepresented visually by thicker lines. The interface 700 indicates aconnectivity matrix with phase locking between channel pairs that have Rindex greater than 0.45. The lines are shown thicker (weighted viarules) that have higher R. Interface elements 908 show multiple raw EEGsignals from different channels. This example includes 8 EEG channels:T3, F7, F8, T4, T5, O1, O2, T6. The brain index value marker 904 rangesbetween 0 and 100 in this example and indicates the network entropy andhow connected the brain is. Various example interfaces for graphicaldisplay will be described. The output from the EEG sensors andprocessing is displayed graphically for the user.

The embodiments of the devices, systems and methods described herein maybe implemented in a combination of both hardware and software. Theseembodiments may be implemented on programmable computers, each computerincluding at least one processor, a data storage system (includingvolatile memory and non-volatile memory or other data storage elementsor a combination thereof), and at least one communication interface.

Program code is applied to input data to perform the functions describedherein and to generate output information. The output information isapplied to one or more output devices. In some embodiments, thecommunication interface may be a network communication interface. Inembodiments in which elements may be combined, the communicationinterface may be a software communication interface, such as those forinter-process communication. In still other embodiments, there may be acombination of communication interfaces implemented as hardware,software, and combination thereof.

Throughout the foregoing discussion, numerous references will be maderegarding servers, services, interfaces, portals, platforms, or othersystems formed from computing devices. It should be appreciated that theuse of such terms is deemed to represent one or more computing deviceshaving at least one processor configured to execute softwareinstructions stored on a computer readable tangible, non-transitorymedium. For example, a server can include one or more computersoperating as a web server, database server, or other type of computerserver in a manner to fulfill described roles, responsibilities, orfunctions.

The following discussion provides many example embodiments. Althougheach embodiment represents a single combination of inventive elements,other examples may include all possible combinations of the disclosedelements. Thus if one embodiment comprises elements A, B, and C, and asecond embodiment comprises elements B and D, other remainingcombinations of A, B, C, or D, may also be used.

The term “connected” or “coupled to” may include both direct coupling(in which two elements that are coupled to each other contact eachother) and indirect coupling (in which at least one additional elementis located between the two elements).

The technical solution of embodiments may be in the form of a softwareproduct. The software product may be stored in a non-volatile ornon-transitory storage medium, which can be a compact disk read-onlymemory (CD-ROM), a USB flash disk, or a removable hard disk. Thesoftware product includes a number of instructions that enable acomputer device (personal computer, server, or network device) toexecute the methods provided by the embodiments.

The embodiments described herein are implemented by physical computerhardware, including computing devices, servers, receivers, transmitters,processors, memory, displays, and networks. The embodiments describedherein provide useful physical machines and particularly configuredcomputer hardware arrangements. The embodiments described herein aredirected to electronic machines and methods implemented by electronicmachines adapted for processing and transforming electromagnetic signalswhich represent various types of information. The embodiments describedherein pervasively and integrally relate to machines, and their uses;and the embodiments described herein have no meaning or practicalapplicability outside their use with computer hardware, machines, andvarious hardware components. Substituting the physical hardwareparticularly configured to implement various acts for non-physicalhardware, using mental steps for example, may substantially affect theway the embodiments work. Such computer hardware limitations are clearlyessential elements of the embodiments described herein, and they cannotbe omitted or substituted for mental means without having a materialeffect on the operation and structure of the embodiments describedherein. The computer hardware is essential to implement the variousembodiments described herein and is not merely used to perform stepsexpeditiously and in an efficient manner.

For simplicity only one server system 100 is shown but system mayinclude more server systems 100 operable to access remote networkresources and exchange data. The server system 100 has at least oneprocessor, a data storage device (including volatile memory ornon-volatile memory or other data storage elements or a combinationthereof), and at least one communication interface. The server system100 components may be connected in various ways including directlycoupled, indirectly coupled via a network, and distributed over a widegeographic area and connected via a network (which may be referred to as“cloud computing”).

For example, and without limitation, the server system 100 may be aserver, network appliance, set-top box, embedded device, computerexpansion module, computer or other computing device capable of beingconfigured to carry out the processes described herein.

The server system 100, exemplary of an embodiment, may include at leastone processor, memory, at least one I/O interface, and at least onenetwork interface.

Each processor may be, for example, any type of general-purposemicroprocessor or microcontroller, a digital signal processing (DSP)processor, an integrated circuit, a field programmable gate array(FPGA), a reconfigurable processor, a programmable read-only memory(PROM), or any combination thereof.

Memory may include a suitable combination of any type of computer memorythat is located either internally or externally such as, for example,random-access memory (RAM), read-only memory (ROM), compact discread-only memory (CDROM), electro-optical memory, magneto-opticalmemory, erasable programmable read-only memory (EPROM), andelectrically-erasable programmable read-only memory (EEPROM),Ferroelectric RAM (FRAM) or the like.

Each I/O interface enables server system 100 to interconnect with one ormore input devices, such as a keyboard, mouse, camera, touch screen anda microphone, or with one or more output devices such as a displayscreen and a speaker.

Each network interface enables server system 100 to communicate withother components, to exchange data with other components, to access andconnect to network resources, to serve applications, and perform othercomputing applications by connecting to a network (or multiple networks)capable of carrying data including the Internet, Ethernet, plain oldtelephone service (POTS) line, public switch telephone network (PSTN),integrated services digital network (ISDN), digital subscriber line(DSL), coaxial cable, fiber optics, satellite, mobile, wireless (e.g.W-Fi, WMAX), SS7 signaling network, fixed line, local area network, widearea network, and others, including any combination of these.

Server system 100 is operable to register and authenticate users (usinga login, unique identifier, and password for example) prior to providingaccess to applications, a local network, network resources, othernetworks and network security devices. server system 100 may serve oneuser or multiple users.

Although the embodiments have been described in detail, it should beunderstood that various changes, substitutions and alterations can bemade herein without departing from the scope as defined by the appendedclaims.

Moreover, the scope of the present application is not intended to belimited to the particular embodiments of the process, machine,manufacture, composition of matter, means, methods and steps describedin the specification. As one of ordinary skill in the art will readilyappreciate from the disclosure of the present invention, processes,machines, manufacture, compositions of matter, means, methods, or steps,presently existing or later to be developed, that perform substantiallythe same function or achieve substantially the same result as thecorresponding embodiments described herein may be utilized. Accordingly,the appended claims are intended to include within their scope suchprocesses, machines, manufacture, compositions of matter, means,methods, or steps.

As can be understood, the examples described above and illustrated areintended to be exemplary only.

Example Glossary of Terms

Functionality Index or Brain Value Index: Abbreviated as FI or BVI. Usedinterchangeably with “normalized entropy” and Functionality Index. Itreflects the original term developed to represent normalized entropyboth in value and its depiction on the right or left side of the curvein the graphical display. It was tested in market research withclinicians and changed to Functionality Index in current graphicaloutput of the algorithms. Please see entropy definition.

Channel: Synonymous with electrode or sensor for gathering physiologicalinformation from a patient or control subject. Refers to either an EKG(heart rate) or EEG (brainwave).

Connectivity: Defined by the phase synchrony value, also known as the Rindex, and is an integer between 0 and 1. Please see phasesynchronization definition.

Connectivity Map: Graphical depiction on a schematic of the 8 electrodes(channels), of the strength of the connectivity (phase synchronization)between each of the possible channel pairs. The threshold of a phasesynchrony index=0.45, calculated from normal adult subjects in the awakestate with eyes open. Four levels of connectivity strength are definedby the following ranges and illustrated with connecting lines ofdifferent colours and thicknesses. Light grey and 1 point thicknessdefines connectivity between a pair of electrodes with a phase synchronyvalue of 0.45 to <0.6; Medium grey and 1.5 point thickness for a phasesynchrony value of 0.6 to <0.8; Dark grey and 2 point thickness for aphase synchrony value of 0.8 to <0.9; and black with a 3 point thicknessfor a phase synchrony value of 0.9 to 1.0<0.6.

Connectivity matrix: Step in the algorithm required for calculatingentropy from the phase synchrony values for each electrode pair. Eachelectrode pair's phase synchrony is compared to the threshold (0.45) andassigned a “0” if it is below the threshold or “1” if it is above thethreshold.

Electrode: Synonymous with channel or sensor for gathering physiologicalinformation from a patient or control subject. Refers to either an EKG(heart rate) or EEG (brainwave).

Entropy: In statistical thermodynamics, entropy is a measure of thenumber of microscopic configurations that a thermodynamic system canhave when in a state as specified by certain macroscopic variables. Inthe case of brain function it is the number of connections betweenneuronal networks in a specific brain state, where the alert, awakestate with eyes open represents the maximum connections and is the totalinformation contained within functional neuronal networks.

Functionality Index. Used interchangeably with “normalized entropy”.This term reflects market research with clinicians who preferred it overthe normalized entropy. It is used in the current version graphicaldisplay of the device.

Montage: Refers to a specific arrangement of EEG electrodes on thescalp. Can be the international 10-20 montage of 10 to 20 electrodes ora subset of these.

Normalized entropy: The normalized entropy is the regular entropy thatis calculated, divided by the maximum entropy (at the peak of thecurve), and multiplied by 100. This gives the clinically useful 0 to 100inverted U-shaped curve. It is synonymous with Functionality Index.

Phase synchrony (synchronization). Evaluates the connectivity between 2oscillating signals, in our case, the EEG waveform output from 2channels. It is an integer value between 0 and 1. It is calculated usingthe Hilbert as follows: where Δθ is the phase difference between twosignals. It is dependent on the length of time specified for thecalculation (1 second running window for our device) and the frequencyof the signal (3 Hz for our device).

Sensor: Synonymous with channel or electrode for gathering physiologicalinformation from a patient or control subject. Refers to either an EKG(heart rate) or EEG (brainwave).

What is claimed is:
 1. A system for real-time brain monitoring foranxiety and panic disorders and response to treatment comprising: aplurality of sensors for acquisition of (near) real-time raw sensor datafor monitoring a patient's brain, each sensor corresponding to achannel; a collector device coupled to the plurality of sensors forpre-processing the real-time raw sensor data; a server having: anacquisition unit to receive sensor data from the collector device; aprocessor to compute using the sensor data, a connectivity matrix havingconnectivity values, a connectivity value for each pair of channels, anda real-time brain value index corresponding to a real-time anxiety stateof the patient; and a presentation unit to generate visual elements foran interface in real-time, the visual elements representing thereal-time brain value index to depict the anxiety state of the patientand a connectivity map for the connectivity matrix, the connectivity mapvisually indicating the channels of the sensors and a connecting linebetween a pair of channels representing a strength of connection betweenthe pair of channels, the server system having a display controller toissue control commands to update the interface using the generatedvisual elements; a display device to display and update the interfacewith the visual elements based on the issued control commands from theserver.
 2. The system of claim 1 wherein the processor is configured tocompute treatment guidance, the treatment guidance providing amonitoring state, and an intervention state for anxiety.
 3. The systemof claim 1 wherein the display device provides feedback data to refineor update the processing by the server.
 4. The system of claim 1 whereinthe server computes phase synchronization value for an angle between therespective pair of channels using the sensor data for the respectivepair of channels, wherein entries of the connectivity matrix are thephase synchronization values the pairs of channels.
 5. The system ofclaim 1 wherein the server generates a boolean connectivity matrix basedon the connectivity matrix, such that an entry of the booleanconnectivity matrix is 0 if a corresponding connectivity value is lowerthan a threshold value, and 1 if a corresponding connectivity value ishigher than the threshold value, wherein the server computes thethreshold value from sensor data for a normal adult with eyes open,wherein a connected channel is defined as entries
 1. 6. The system ofclaim 1 wherein the brain value index may be computed based a totalnumber of possible pairs of channels given a specific channel montageN=Nc!/2!(Nc−p)!, Nc being a number of channels, p being a number ofconnected pairs of channels, p being calculated using a threshold valueand the connectivity values of the connectivity matrix, wherein theserver system computes an entropy value associated of the p values andcalculates a normalized entropy to a value between 0 and
 1. 7. Thesystem of claim 1 wherein the server implements machine learning tocompute the brain value index based on historical data for the patientor other patients.
 8. The system of claim 1 wherein the server computestreatment guidance using the brain value index, the treatment guidancetriggers treatment for organ donation upon detecting that the patient isa candidate for the organ donation by evaluating the brain value indexusing an organ donation threshold value.
 9. The system of claim 1wherein the server computes treatment guidance using the brain valueindex, wherein the treatment guidance provides a monitoring state, anintervention state and a resuscitate state.
 10. The system of claim 1wherein the display device provides feedback data to refine or updatethe computations by the server, the feedback data confirming theaccuracy of the brain value index.
 11. The system of claim 1 wherein theserver computes treatment guidance using the brain value index, whereinthe server implements machine learning to generate recommendedtreatments as part of the treatment guidance based on historical datafor the patient or other patients.
 12. The system of claim 1 wherein thereal-time raw sensor data is linked with a patient identifier and timeindicia.
 13. The system of claim 1 wherein the interface comprises agraph of raw EEG signals per channel over time and a listing of theconnectivity valuesover time or at a point in time.
 14. The system ofclaim 1 wherein the server is configured to generate the interface toinclude visual elements depicting the channels, connections between thechannels, and strengths of the connections.
 15. The system of claim 1wherein the server is configured to generate the interface to includevisual elements depicting a curve and a marker for the brain value indexat a position along the curve at a point in time, the positionindicating the brain state.
 16. A system for real-time brain monitoringcomprising: a plurality of sensors for acquisition of (near) real-timeraw sensor data for monitoring a patient's brain, each sensorcorresponding to a channel; a collector device coupled to the pluralityof sensors for pre-processing the real-time raw sensor data; a serverwith an acquisition unit to receive sensor data from the collectordevice, a processing unit to compute a connectivity matrix havingconnectivity values, a connectivity value for each pair of channels, areal-time brain value index for anxiety and treatment data using thesensor data; and a presentation unit to generate visual elements for aninterface in real-time, the visual elements representing a connectivitymap for the connectivity matrix, the real-time brain value index and theanxiety and treatment data, the visual elements depict the channels,connections between the channels, and strengths of the connections, theserver system having a display controller to issue control commands toupdate the interface, the brain value index corresponding to a real-timeanxiety state of the patient, the visual elements to indicate thereal-time anxiety state of the patient; a display device to display andupdate the interface with the visual elements based on the issuedcontrol commands from the server.
 17. The system of claim 16 wherein thetreatment guidance provides a monitoring state, an intervention stateand a resuscitate state.
 18. The system of claim 16 wherein the displaydevice provides feedback data to refine or update the computations bythe server, the feedback data confirming the accuracy of the brain valueindex.
 19. The system of claim 16 wherein the server generates theconnectivity map using the connectivity matrix, the connectivity mapvisually indicating the channels of the sensors and a connecting linebetween a pair of channels representing a strength of connection betweenthe pair of channels.
 20. A processing device for real-time brainmonitoring of anxiety comprising: a network interface for acquisition ofreal-time raw sensor data for a patient's brain; a server for processingthe real-time raw sensor data to compute a connectivity matrix havingconnectivity values, a connectivity value for each pair of channels, anda real-time brain value index, the server for generating visual elementsfor an interface in real-time, the visual elements representing aconnectivity map for the connectivity matrix, the real-time brain valueindex for anxiety, the server system having a display controller toissue control commands to update the interface, the brain value indexcorresponding to a real-time anxiety state of the patient; a storagedevice for storing computed real-time brain value indices for anxiety;and a display device having the interface to generate and update avisual representation the real-time brain value index based on theissued control commands from the server.